Robotics, Machine Learning (ML) and AI is starting to dominate the enterprise, service providers and consumer worlds for decades to come. We are entering to perhaps another major showdown for use of technology using Artificial Intelligence and Robotics with massive amount of sensors for years to come and I predict number of sensors in entire world economy will exceed 1T by end of 2030 time-frame and this will generate level of innovation and growth in enterprises, consumers and governments which we had not seen except for industrial revolution in 20th century.

Machine learning has been redefining how even the basics of operational tasks are done across industries. The financial industry is no different. While some of the applications of machine learning in finance are clearly visible to us - like mobile banking apps and chatbots, the technology is now being gradually used for drawing out accurate historical data of customers and predicting their future needs as well.

The eCommerce industry is growing by manifolds across the world. From what started as a few stores that enabled online shopping; today, the smallest of brands are able to take their products online and market them to a large consumer base. Call it the ease of technology and the ability to use data, almost every eCommerce store is able to capture a segment of the consumer market - despite the rising competition.

RPA software has proven to reliably reduce costs by removing manual work from various business workflows and processes. But is RPA adoption by all enterprises need to automate their business processes? What else does process automation have in store other than RPA? To answer these questions, it helps to understand where RPA technologies came from and at what capabilities they now offer. Using machine-learning platforms to also incorporate new information gathered from background collection of workflow exceptions is the most practical next step to achieving full automation. We have far to go before RPA fulfills its “robotic” mission of removing the human element.

You should know about Artificial Intelligence and Machine Learning in the healthcare industry and how it will impact our future. These technologies WILL dramatically change the way we work in healthcare. As the use of Machine Learning grows in healthcare, continue to obsess over the privacy of your customer data. Making “cool” innovations in Artificial Intelligence or Machine Learning won’t work if not coupled with a relentless pursuit to serve the customer. These endeavors are expensive, so spend your IT budget wisely, ensuring new innovation creates true value and is easy for the end user.

AI-based technology will fundamentally change economies, politics, the planet, and indeed humanity. Even today we are only just beginning to see some of these changes come to fruition. For better or for worse, society will be permanently altered due to artificial intelligence. Just think of the dramatic changes we’ve witnessed just in our own lives as the age of the Internet has disrupted the landscape. Given the dramatic pace of innovation today, one can’t help but wonder what humanity might look like in a few decades as compared to today. How will we, as a society, fare in the brave new world of tomorrow?

ALL businesses are in need of digitization, with the vast majority eagerly trying to to find a digitization strategy and trusted partners to help them get there quickly. The time for digitization is now, and not making this a core part of your business's strategy in 2018 is not just dangerous, it is fatal. The good news is, there are a lot of great partners out there to help you along, picking the right one is just another part of your Digital Transformation Story. HOW you tell that story, while understanding exceedingly changing industry environments, the data realities of the current condition today, and the human/machine capital needed to drive initiatives, is immensely valuable.

As the amount of structured and unstructured data explodes, the financial sector is realizing the necessity of harnessing and analyzing that data in the fastest, most effective way possible in order to stay competitive. A revolution can be defined as a fundamental change in an organizational structure that takes place in a relatively short period of time when people “revolt” against the current order. Currently, the financial sector is making a massive shift towards big data and machine learning technology and applied solutions. Here are five signs that this is the beginning of a revolution in finance

Watching the bitcoin phenomenon is a bit like watching the three-decade decline of the internet from a playspace for the counterculture to one for venture capitalists. We thought the net would break the monopoly of top-down, corporate media. But as business interests took over it has become primarily a delivery system for streaming television to consumers, and consumer data to advertisers. Likewise, Bitcoin was intended to break the monopoly of the banking system over central currency and credit. But, in the end, it will turn into just another platform for the big banks to do the same old extraction they always have. Here’s how.

The introduction of PFE is the beginning of a revolution in relations between the bank and its clients. The insights that flow from it will primarily build new value for users, intrinsically bonding them with the bank. Along with the development of artificial intelligence algorithms, more and more sophisticated ways will emerge that will pre-empt their clients' behaviour and support them in everyday life. There will be ideas for dynamic adjustment of the bank's communication to key moments in the life of the user, providing summaries after international trips, gift expenses, car running costs since the last refueling or a summary of taxi expenses.

Learn the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. In this post, the famous Iris flower data set is used for creating a machine learning model to classify species of flowers. In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. Following the steps, you will deploy your model as an API, test it and retrain by creating a feedback data connection.

Fraud analytics can identify the current behaviour and help in fraud detection whereas applying this knowledge in a model of predictive analytics can help in fraud prevention. Since tasks like data extraction and pre-processing are of paramount importance, we would need data scientists who possess not only a technical knowhow but more importantly patience, perseverance, critical thinking, and domain understanding. In here, the imputation for missing values may not be required but reported for certain attributes. Even when required, it may not be as easy and straightforward as in the different problem statements, especially when a few indicators are about to raise a red flag.

Data ingestion and big data storage were the most foreign to marketing leaders. Understanding where each team sat in the organizations' data story, was incredibly powerful and seemed to inspire the accountability and permission for business leaders to engage in a more informed and strategic technology conversation with IT. CIOs and CMOs must share each other's mindset in how data plays a part in the organizations business strategy, and if this isn't the case, both will end up overspending and over allocating budget and talent in the quest to "be an analytical organization". Here are some items for marketing leaders to explore if all of this sounds familiar:

Artificial intelligence is an incredibly complicated concept for application testing. There aren’t that many products that offer real AI/machine learning functionality for app QA. Your best bet is to find a QA team that has in-house machine learning solutions or uses one of the tools that we mentioned and their alternatives. This way, your app testing needs will get the maximum coverage that they deserve. It’s also important to remember that traditional QA automation still works. You don’t have to jump on the AI bandwagon just because everyone is using it in their marketing nowadays.

Travel and tourism is on the rise globally. The industry now accounts for more than one-tenth of the world’s GDP. Interestingly, the target market is not only from developed nations but also from the emerging parts of the world that boast of increasing disposable incomes and a strong desire to explore cultures outside their own.

When there’s a bust, we expect a backlash that leaves people uncertain of the future potential for Bitcoin, cryptocurrencies, and ICOs. Meanwhile, the underlying buildout of the blockchain infrastructure that’s being created to support all this will ultimately enable a more trusted world. How long it takes to create that trust depends on more than technology. It depends on us understanding these cycles and how to handle the inevitable human emotions of fear and greed that inflate and burst the bubble. Hopefully, this article has prepared you to think for yourself beyond those emotions.

Cognitive Brain Cloud Computing coupled with Blockchain Based security models is having a moment using latest technical jargon in IT world. Artificial neural network algorithms like deep learning, which are very loosely based on the way the human brain operates, now allow digital computers to perform such extraordinary feats as translating the language, hunting for subtle patterns in huge amounts of data, and beating the best human players at Go. But even as engineers continue to push this mighty computing strategy, the energy efficiency of digital computing is fast approaching its limits.

Many people have been wondering whether blockchain will be the new paradigm in digital certification for the education and training world. As the buzz spreads, some already seem convinced that integrating blockchain technology into the issuance of academic records such as diplomas, affidavits, certificates, transcripts, etc. will be an effective response to the legal and technological expectations and constraints of the education and training world. It is of the utmost urgency to clarify the advantages, disadvantages, and limitations of blockchain in digital certification. Like all technology, its role is to provide a concrete, serious solution to real challenges, one that can integrate the existing environments of the different users and their equipment, their way of operating, and their organization.

The increasing security threat surface is a major challenge for businesses, particularly those functioning in the BFSI sector. There has been a lot of news of frauds as well as hacking from this sector, which is giving Chief Information Security Officers sleepless nights. Digital deployments bring with them increased vulnerabilities. The attack surface has increased because of the extensions within enterprise. Frequency and sophistication of cyber threats are continuously growing. In the evolving threat landscape, digital transformation technologies such as Artificial Intelligence and Machine Learning hold a great deal of hope.

Data analysis helps to make sense of our data otherwise they will remain a pile of unwieldy information; perhaps a pile of figures. This is essential because analytics assist humans in making decisions. Therefore, conducting the analysis to produce the best results for the decisions to be made is an important part of the process, as is appropriately presenting the results. Its an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analysing and dissecting data, and presenting the findings in an easily-digestible format.

When changes are made to any transaction or agreement it will be visible to the parties involved in the specific transaction, which minimize your chances of blunders. In principle, Blockchain technology provides Authenticating an identity, Establishing Contracts, and Recording Transactions. Blockchain technology can be applied in a lot of different fields. However, it must be adjusted to fit the requirements of a specific business. The discussion of Blockchain technology will keep on escalating as more people are developing and understanding the applications and capabilities of the Blockchain.

While IoT has many advantages, enterprises need to overcome some important problems in cloud computing to fully gain from these potential advantages. It’s a known fact that IoT and the cloud are impossible to separate—but only about a third of the data collected by the growing army of sensors is analyzed at source. While IoT has many advantages, enterprises need to overcome some important problems in cloud computing to fully gain from these potential advantages. Fog computing facilitates the operations of computing, storage, and networking services between end devices and cloud computing data centers.

Most companies get caught up in the first few stages of product development and miss out by not planning for Stage 4 sources of value. The challenge for manufacturers aiming to profit from IoT opportunities is to manage their product development road-map strategically. They have to anticipate solution “mash-ups” and data from different ‘vertical’ silos or third-party sources. The supply-side of the IoT market faces its own challenges. Basic connectivity will be commoditised once technology choices are simplified. By then, network and platform interoperability will drive value through new business models based on shared resources and data assets.

When the concept of AI was first introduced, the HR departments were not completely convinced by it, as they feared a heavy loss in the number of jobs because of the increased dependence on machines. But gradually the organizations have opened up to it. Technologies and tools like cloud computing, business analytics, e-recruitment, CPM (Computerized Performance Monitoring) have minimized the labor of HR personnel and given them considerable time to focus on other goals. Now the question arises, that if AI is such a convenience for the HR, then what is the debate all about?

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The Harvard Innovation Lab

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